This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF tasks. Then we propose to imp…
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48 results for “CFS”
CF-moves for virtual linksmath.GT
The paper extends CF-moves to classify virtual links of any number of components.
problem Classifying virtual links using CF-moves.
method Extending CF-moves to classify virtual links of arbitrary number of components using the virtual linking number and invariants.
result Classification of 3-component even virtual links up to CF-moves.
Correlation filters (CFs) are a class of classifiers that are attractive for object localization and tracking applications. Traditionally, CFs have been designed in the frequency domain using the discrete Fourier transform (DFT), where correlation is efficiently implemented. However, existing CF designs do not account …
Survey on concept factorization methods for better feature learning.
problem Understanding the quality of learned features in representation learning.
method Categorizing and summarizing recent advances in concept factorization methods.
result Provides insights into theoretical basis and current developments in concept factorization.
In 1985, physicists Dixon, Harvey, Vafa and Witten studied string theories on Calabi-Yau orbifolds (cf. [DHVW]). An interesting discovery in their paper was the prediction that a certain physicist's Euler number of the orbifold must be equal to the Euler number of any of its crepant resolutions. This was soon related t…
We formulate a sufficient condition for the existence of a consistent price system (CPS), which is weaker than the conditional full support condition (CFS) introduced by Guasoni, Rasonyi, and Schachermayer [Ann. Appl. Probab., 18(2008), pp. 491-520] . We use the new condition to show the existence of CPSs for certain p…
Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classificationcs.LG
This work benchmarks counterfactual methods in time series classification.
problem Lack of benchmarking studies for counterfactual methods in time series classification.
method Redesign metrics for sparsity, plausibility, and consistency; systematically benchmark 6 CF methods on 30 datasets.
result Performance of CF methods varies across metrics and models.
Study shows no DNN avoids catastrophic forgetting in real-world tasks.
problem Catastrophic forgetting in Deep Neural Networks (DNNs).
method Empirical study on large-scale visual classification datasets, enforcing typical application constraints.
result No model avoids catastrophic forgetting across all datasets and tasks under application conditions.
CF-INNs can approximate any invertible function, resolving a long-standing problem.
problem Whether CF-INNs can approximate any invertible function.
method Demonstrated CF-INNs are universal approximators for invertible functions by showing a convenient criterion.
result CF-INNs are universal approximators for invertible functions.
Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains. Many traditional methods focus on enriching compared neighborhood relations in CF directly to address the sparsity problem. In this paper, we propose superhighway const…
We consider canonical metrics on Fano manifolds. First we introduce a norm-type functional on Fano manifolds, which has Kahler-Einstein or Kahler-Ricci soliton as its critical point and the Kahler-Ricci flow can be viewed as its (reduced) gradient flow. We then obtain a natural lower bound of this functional. As an app…
Paper benchmarks CF mitigation in federated time series forecasting.
problem Catastrophic forgetting in federated learning for time series forecasting.
method Comprehensive evaluation of CF mitigation strategies in federated time series forecasting.
result Introduction of a new benchmark for CF in time series federated learning.
The paper tackles CF in CL by analyzing NTK overlap matrix and proposing OGD.
problem Catastrophic Forgetting in continual learning.
method Analysis of NTK overlap matrix, OGD with PCA.
result Proposes OGD to mitigate CF, supported by experiments.
Efficient CF approach using fast adaptive PCA for recommender systems.
problem Matrix completion problem in recommender systems.
method Fast adaptive randomized singular value decomposition (SVD) and termination mechanism for latent factors.
result The approach achieves near optimal prediction accuracy with high runtime efficiency.
A non-parametric method for evaluation of the aggregate loss distribution (ALD) by combining and numerically inverting the empirical characteristic functions (CFs) is presented and illustrated. This approach to evaluate ALD is based on purely non-parametric considerations, i.e., based on the empirical CFs of frequency …
DiCFS improves CFS for big data, handling large datasets efficiently.
problem Efficient feature selection for large datasets in big data.
method Distributed CFS (DiCFS) using Apache Spark for scalability and parallel processing.
result DiCFS outperforms WEKA in terms of time-efficiency and scalability.
Cluster Forestsstat.ME
With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method---Cluster Forests (CF) is proposed. Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments fo…
Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, w…
Recommender systems (RS) help users navigate large sets of items in the search for "interesting" ones. One approach to RS is Collaborative Filtering (CF), which is based on the idea that similar users are interested in similar items. Most model-based approaches to CF seek to train a machine-learning/data-mining model b…
While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions …
A neural network method estimates densities from characteristic functions.
problem Estimating fixed-horizon probability densities from empirical characteristic functions.
method Data-driven Fourier-mixture neural-network method trained in Fourier space.
result Competitive performance and clear gains on heavy-tailed targets.
Integrates CF and RL for collaborative recommendation.
problem Interactive recommendation problem where current recommendations affect future quality.
method Develops CFRL, a novel approach that models as an RL task and learns optimal policy.
result Demonstrates superior performance compared to existing methods on real-world datasets.
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of the conditions under which item-based recommendations will succeed or fail. We investigate the existence of an ideal item-based CF method a…
In this short note we study nonexistence result of biharmonic maps from a complete Riemannian manifold into a Riemannian manifold with nonpositive sectional curvature. Assume that is a biharmonic map, where is a complete Riemannian manifold and a Riemannian manifold with nonpositive…
The paper studies geometric properties of -harmonic maps and proves Liouville type results.
problem Exploring geometric properties of -harmonic maps.
method Unified geometric analytic methods, first and second variation formulas, stress-energy tensor, conservation law, monotonicity formula, asymptotic assumption, extrinsic average variational method.
result Proves Liouville type results for -harmonic maps.
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based …
New curvature condition helps characterize Kähler manifolds.
problem Characterize compact Kähler manifolds with specific curvature properties.
method Introduce and utilize positive bisectional curvature condition.
result Deduce characterization theorem for manifolds with positive bisectional curvature.
Develops a new method for equivariant Lagrangian Floer homology using symplectic homotopy quotients.
problem Constructing equivariant Lagrangian Floer homology for symplectic manifolds with group actions.
method Using symplectic homotopy quotients involving cotangent bundles of an approximation of , and Wehrheim and Woodward's theory of quilts.
result Shows that the constructed groups are independent of auxiliary choices and are -bimodules.
The paper defines a new Lie groupoid concept for infinite dimensions.
problem Obstacles in infinite-dimensional Lie groupoids.
method Introduces a new notion of 'bi-algebroid' for infinite dimensions.
result Recover partial Poisson manifolds and Banach Poisson Lie groups.
Proposes CF-SFL to improve sparse data recommendation.
problem Poor performance of CF in sparse data.
method Generative user feedback loop to simulate user feedback.
result Improves recommendation results on multiple datasets.
The paper proposes a fair reinforcement learning framework to prevent healthcare disparities.
problem Unfair reinforcement learning policies in healthcare can lead to socioeconomically-disadvantaged subgroups being underprivileged.
method The paper introduces a counterfactual fairness framework and a sequential data preprocessing algorithm to achieve fair sequential decision making.
result The proposed approach greatly enhances fair access to counseling in a digital health dataset designed to reduce opioid misuse.
A new method for finding efficient neural interaction functions in collaborative filtering.
problem Finding consistent good performance for complex interactions in collaborative filtering.
method Proposes a search algorithm for simple neural interaction functions (SIF) in CF, using a structured multi-layer perceptron.
result Demonstrates much better prediction performance and distinct IFCs for different data sets and tasks.
In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it's almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied…
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
New energy functional and fields for Yang-Mills theory, proving monotonicity and vanishing theorems.
problem Developing new mathematical tools for Yang-Mills theory.
method Introducing normalized exponential Yang-Mills energy functional, deriving monotonicity formula and vanishing theorem.
result Monotonicity and vanishing theorems for exponential Yang-Mills fields.
CF-GNN provides reliable uncertainty estimates for graph data.
problem Lack of rigorous uncertainty estimates in GNNs.
method Conformal prediction extended to graph-based models (CF-GNN).
result CF-GNN produces prediction sets with provable coverage probability.
Flexible priors improve VAE-based CF models for better user preference modeling.
problem Simplistic priors in VAEs limit user preference modeling and deeper representation learning.
method Incorporated flexible priors and gating mechanisms into VAEs for collaborative filtering.
result Flexible priors and gating mechanisms significantly improve recommendation performance.
Learning policies on data synthesized by models can in principle quench the thirst of reinforcement learning algorithms for large amounts of real experience, which is often costly to acquire. However, simulating plausible experience de novo is a hard problem for many complex environments, often resulting in biases for …
Improved product recommendations using deep learning.
problem Sparse customer purchasing data for personalized recommendations.
method Deep Collaborative Filtering (NCF) with latent variables and Bayesian Optimization.
result NCF achieved highest NDCG performance on proprietary dataset.
Proposes intrinsic methods to detect overfitting in models.
problem Detecting overfitting in models without relying on external test sets.
method Counterfactual Simulation (CFS) to analyze model flow through training data.
result CFS can separate models with different levels of overfit using only their logic circuit representations.
A new GAN model uses characteristic functions to improve image generation.
problem Improving stability and diversity in GANs for complex distributions.
method Integrates characteristic functions to compare distributions directly, stabilizes training, and uses auto-encoder structure.
result Proposes RCF-GAN achieving superior image generation and reconstruction.
Paper addresses feasibility of counterfactual explanations in ML models, especially for critical domains.
problem Feasibility of counterfactual examples in ML models, especially in healthcare and finance.
method Uses partial structural causal models and modified variational autoencoder loss to generate counterfactuals that satisfy feasibility constraints.
result Generated counterfactuals better satisfy feasibility constraints than existing methods.
Centroids Matching tackles catastrophic forgetting by matching feature vectors to class centroids.
problem Catastrophic forgetting in neural networks when learning new tasks.
method Centroids Matching operates in the embedding space of neural network features, matching these vectors to class centroids.
result Centroids Matching achieves high accuracy on all tasks without using external memory, even in realistic scenarios.
CL methods improve monolingual ASR models across new tasks without forgetting past data.
problem Catastrophic Forgetting in monolingual ASR models when adapting to new domains or accents.
method Implement and compare various Continual Learning methods for monolingual ASR.
result Best CL method reduces performance gap by over 40% with minimal past data.
CGAs estimate team performance from data, simplifying SV computation.
problem Predicting and rewarding team performance using game theory.
method Cooperative game abstractions (CGAs) for estimating characteristic functions from data.
result CGAs enable linear-time computation of Shapley Value for team contributions.
CF-VAE models capture multi-modal distributions for better structured sequence prediction.
problem Challenges in capturing multi-modality of future states in latent variable models.
method Conditional Flow Variational Autoencoders (CF-VAE) with conditional normalizing flows.
result CF-VAE achieves state-of-the-art results on multi-modal structured sequence prediction datasets.
WCF uses Wasserstein distance to recommend cold-start items based on content similarity.
problem Recommendation performance drops for new items with little interaction history.
method Applies Wasserstein distance to map interaction history to contents, inferring user preferences.
result WCF outperforms state-of-the-art methods in cold-start recommendation.
Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users. However, CF-based methods are known for suffering from cold start problems and data sparsity proble…